pacman::p_load(sf, tidyverse, tmap, spdep, readr, dplyr, tidyr,funModeling)Take Home Exercise 1
1 Overview
1.1 Getting Started
In the code chunk below, p_load() of pacman package is used to install and load the following R packages into R environment:
sf is use for importing and handling geospatial data in R,
tidyverse is mainly use for wrangling attribute data in R,
tmap will be used to prepare cartographic quality chropleth map,
spdep will be used to compute spatial weights, global and local spatial autocorrelation statistics, and
funModeling will be used for rapid Exploratory Data Analysis
1.2 Importing Geospatial Data
In this in-class data, two geospatial datasets will beused, they are:
geo_export
nga_ADM2
1.2.1 Importing Geospatial Data
First, we are going to import the water point geospatial data (i.e. geo_export) by using the code chunk below.
# wp <- st_read(dsn = "data",
# layer = "geo_export",
# crs = 4326) %>%
# filter(clean_coun == "Nigeria")Things to learn from the code chunk above:
st_read()of sf package is used to import geo_export shapefile into R environment and save the imported geospatial data into simple feature data table.filter()of dplyr package is used to extract water point records of Nigeria.
Next, write_rds() of readr package is used to save the extracted sf data table (i.e. wp) into an output file in rds data format. The output file is called wp_nga.rds and it is saved in geodata sub-folder.
write_rds(wp, "data/wp_nga.rds")1.2.2 Import Nigeria LGA Boundary data
Now, we are going to import the LGA boundary data into R environment by using the code chunk below.
nga <- st_read(dsn = "data",
layer = "geoBoundaries-NGA-ADM2",
crs = 4326)Thing to learn from the code chunk above.
st_read()of sf package is used to import nga_admbnda_adm2_osgof_20190417 shapefile into R environment and save the imported geospatial data into simple feature data table.
1.3 Data Wrangling
1.3.1 Recoding NA values into string
In the code chunk below, replace_na() is used to recode all the NA values in status_cle field into Unknown.
wp_nga <- read_rds("data/wp_nga.rds") %>%
dplyr::mutate(status_cle =
replace_na(status_cle, "Unknown"))1.3.2 EDA
In the code chunk below, freq() of funModeling package is used to display the distribution of status_cle field in wp_nga.
freq(data=wp_nga,
input = 'status_cle')The above bar chart provide a brief understanding that the percentage of water-points that are functional in Nigeria is slightly less than 50%. It is crucial thus to dive deeper to determine if there are significant pattern in areas that do not have functional water-points and if the neighbouring areas can support those areas that face scarcity in water supply.
Observe that there are two categories with similar names (i.e. ‘Non-functional due to dry season’ and ‘Non functional due to dry season’, we will standardise this by shanging that later to ‘Non-functional due to dry season’.
1.4 Extracting Water Point Data
In this section, we will extract the water point records by using classes in status_cle field.
1.4.1 Extracting functional water point
In the code chunk below, filter() of dplyr is used to select functional water points.
wpt_functional <- wp_nga %>%
filter(status_cle %in%
c("Functional",
"Functional but not in use",
"Functional but needs repair"))freq(data = wpt_functional,
input = "status_cle")1.4.2 Extracting non-functional water point
In the code chunk below, filter() of dplyr is used to select non-functional water points.
wpt_nonfunctional <- wp_nga %>%
filter(status_cle %in%
c("Abandoned/Decommissioned",
"Abandoned",
"Non-Functional",
"Non functional due to dry season",
"Non-Functional due to dry season"))freq(data=wpt_nonfunctional,
input = 'status_cle')1.4.3 Extracting water point with Unknown class
In the code chunk below, filter() of dplyr is used to select water points with unknown status.
wpt_unknown <- wp_nga %>%
filter(status_cle == "Unknown")1.5 Performing Point-in-Polygon Count
nga_wp <- nga %>%
mutate(`total wpt` = lengths(
st_intersects(nga, wp_nga))) %>%
mutate(`wpt functional` = lengths(
st_intersects(nga, wpt_functional))) %>%
mutate(`wpt non-functional` = lengths(
st_intersects(nga, wpt_nonfunctional))) %>%
mutate(`wpt unknown` = lengths(
st_intersects(nga, wpt_unknown)))1.6 Saving the Analytical Data Table
nga_wp <- nga_wp %>%
mutate(pct_functional = `wpt functional`/`total wpt`) %>%
mutate(`pct_non-functional` = `wpt non-functional`/`total wpt`) %>%
dplyr::select(1, 6:10)Things to learn from the code chunk above:
mutate()of dplyr package is used to derive two fields namely pct_functional and pct_non-functional.to keep the file size small,
select()of dplyr is used to retain only field 1, 6,7,8,9 and 10.
Now, you have the tidy sf data table subsequent analysis. We will save the sf data table into rds format.
write_rds(nga_wp, "data/nga_wp.rds")1.7 Visualising the Spatial Distribution of Water Points
nga_wp <- read_rds("data/nga_wp.rds")
total <- qtm(nga_wp, "total wpt")
wp_functional <- qtm(nga_wp, "wpt functional")
wp_nonfunctional <- qtm(nga_wp, "wpt non-functional")
unknown <- qtm(nga_wp, "wpt unknown")
tmap_mode("view")
tmap_arrange(total, wp_functional, wp_nonfunctional, unknown, asp=1, ncol=2)Based on the above chart, we observe that in terms of functional water-points the north-west zone has the most functional water-points the number of non-functional water-points seems to be scattered all over in Nigeria.
It is interesting to note that while the district Ifelodun has a relatively higher number of functional waterpoints, it also has the highest number of non-functional waterpoints.
In terms of unknown waterpoint statuses it it mostly populated in the north-central zone of Nigeria.
Summary Statistics of the data
First we will take a look the dataset.
head(nga_wp, n= 10)Simple feature collection with 10 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 3.005022 ymin: 4.888055 xmax: 13.83477 ymax: 13.71406
Geodetic CRS: WGS 84
shapeName total wpt wpt functional wpt non-functional wpt unknown
1 Aba North 17 7 9 1
2 Aba South 71 29 35 7
3 Abadam 0 0 0 0
4 Abaji 57 23 34 0
5 Abak 48 23 25 0
6 Abakaliki 233 82 42 109
7 Abeokuta North 34 16 15 3
8 Abeokuta South 119 72 33 14
9 Abi 152 79 62 11
10 Aboh-Mbaise 66 18 26 22
geometry
1 MULTIPOLYGON (((7.401109 5....
2 MULTIPOLYGON (((7.334479 5....
3 MULTIPOLYGON (((13.83477 13...
4 MULTIPOLYGON (((7.045872 9....
5 MULTIPOLYGON (((7.811244 5....
6 MULTIPOLYGON (((8.4109 6.28...
7 MULTIPOLYGON (((3.143903 7....
8 MULTIPOLYGON (((3.301615 7....
9 MULTIPOLYGON (((8.153282 5....
10 MULTIPOLYGON (((7.321909 5....1.8.1 Top 10 areas with the most functional waterpoints
top_func <- nga_wp %>%
dplyr::top_n(10, `wpt functional`) %>%
dplyr::select(shapeName, `wpt functional`)
top_funcSimple feature collection with 10 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 8.646937 ymin: 11.61986 xmax: 10.59487 ymax: 13.13825
Geodetic CRS: WGS 84
shapeName wpt functional geometry
1 Auyo 500 MULTIPOLYGON (((10.05964 12...
2 Babura 752 MULTIPOLYGON (((9.037085 12...
3 Biriniwa 645 MULTIPOLYGON (((10.2548 13....
4 Gagarawa 412 MULTIPOLYGON (((9.690529 12...
5 Kaugama 520 MULTIPOLYGON (((9.886559 12...
6 Kiri Kasamma 399 MULTIPOLYGON (((10.259 12.4...
7 Kiyawa 460 MULTIPOLYGON (((9.660932 11...
8 Nguru 405 MULTIPOLYGON (((10.33446 12...
9 Sule-Tankarkar 420 MULTIPOLYGON (((9.028389 12...
10 Taura 591 MULTIPOLYGON (((9.510234 12...plot(nga_wp)
1.9 Transforming the projection of preschool from wgs84 to svy21
In geospatial analytics, it is very common for us to transform the original data from geographic coordinate system to projected coordinate system. This is because geographic coordinate system is not appropriate if the analysis need to use distance or/and area measurements.
The print below reveals that the assigned coordinates system is WGS 84, the 'World Geodetic System 1984' which is inappropriate in our case and should be using the CRS of Nigeria with an ESPG code of either 26391, 26392, and 26303. A country's epsg code can be obtained by referring to epsg.io.
We will just use the EPSG code of 26391 in our analysis.
st_geometry(nga_wp)Geometry set for 774 features
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84
First 5 geometries:Therefore, we need to reproject nga_wp from one coordinate system to another coordinate system mathemetically using the st_transform function of the sf package, as shown by the code chunk below.
nga_wp26391 <- st_transform(nga_wp, crs = 26391)Next, we will view the content of nga_wp26391 sf data frame as shown below.
st_geometry(nga_wp26391)Geometry set for 774 features
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 28879.72 ymin: 30292.37 xmax: 1343798 ymax: 1094244
Projected CRS: Minna / Nigeria West Belt
First 5 geometries:Notice that instead of Geodetic CRS it has been changed to a Projected CRS of Minna / Nigeria West Belt.
Limitations/ Further work
For future work to consider demarcate the different regions in Nigeria as outline below to understand better if certain region faced water shortage more severely than other regions.